Helping decision-makers explore data with dashboard

AHDB Pork had yesterday a very interesting news release, urging pork producers to take advantage of the data produced by the British Pig Health Scheme (BPHS), a health monitoring scheme that screens pigs at slaughter. Producers analysing their herds’ post-mortem data will over time accrue valuable baseline information regarding their herd health, in particular in relations to subclinical diseases which can have a significant impact on both a unit’s physical and financial performance.

I am a loud advocate of health monitoring schemes such as the BPHS and a firm believer in the value of data collected in slaughterhouses. I do, however, also recognise that the prospect of data exploration and interpretation can be more than a little intimidating when you are trying to look for meaningful relationships in something that looks like this.

Ahhhhhhhhh, the good old spreadsheet… both reassuringly familiar and scarily inefficient at the same time… Can we blame decision-makers for failing to consider all relevant information, when such information is drowned in a hard-to-navigate sea of numbers?

No we can’t and this is why data scientists like me have to come up with analytical and visualisation tools which allow decision-makers, whether on a farm or at the head of a big company, to unlock the potential of their data.

The picture above is a screenshot of an interactive dashboard I created for you to explore the DEFRA milk statistics. To start exploring the dashboard, click here.

The different tabs of the dashboard allows you to access the data relating to milk production on one hand, and the data on milk disposal on the other hand. A series of filters on the left creates reactive graphs so that you only see what you ask for, when you ask for it. Clever!

For the purpose of this post, I created a relatively simple dashboard. A different layout or extra capabilities may be required to efficiently “peel” all layers in your data and visualise the answers to your most important questions.

PS: I’ll add some technical points for those interested. The dashboard above is aShiny app developed in R. I find them to be powerfully interactive and accessible to beginners as no HTML, CSS, or JavaScript knowledge is required (although you can go a long way with personalising your app if you know these languages, e.g the dashboard has the Epi-Connect colour scheme running throughout thanks to some CSS coding). Shinyapps.io offers a simple way to host your app publicly although it can, of course, be hosted on private servers if you wish to restrict access to private data. For those of you interested to have a look at the R code used to create this app, I provide the code free of charge on Epi-Connect’sGithub.